The hottest Substack posts of depression2022

And their main takeaways
39 implied HN points 29 Jan 24
  1. A Hong Kong court orders China's Evergrande Group to liquidate, impacting creditors and homebuyers.
  2. Evergrande's $300 billion debts are massive, rivaling Hong Kong's GDP.
  3. The Chinese property market is distressed with major developers facing default, potentially affecting capital market accessibility for Chinese firms in the future.
39 implied HN points 25 Jan 24
  1. PayPal announced new innovations at an event, like a one-click checkout product and making Venmo more business-friendly.
  2. The stock initially rose but then fell after the announcement, indicating mixed reactions from the world.
  3. The new CEO highlighted the potential for improvement by implementing simple, customer-focused changes, which could positively impact PayPal's business.
39 implied HN points 09 Jan 24
  1. InVision, a popular design collaboration app, is shutting down after facing stiff competition from Figma.
  2. The closure of InVision highlights the risks of startup investing and the challenges of managing investments without acquisitions or IPOs.
  3. 2024 may see more unicorn failures than successful IPOs, with many overfunded startups expected to fail.
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19 implied HN points 02 Feb 24
  1. Meta exceeded earnings expectations for Q4 2023 with $40.1B in revenue and an EPS of $5.33.
  2. Meta reported a 25% YoY revenue growth in Q4, reduced expenses by 8%, and achieved a 41% operating margin.
  3. Meta announced plans to pay $0.5 quarterly dividends and buy back $50B of stock, emphasizing continued focus on the Metaverse and AI development.
0 implied HN points 17 Jan 24
  1. A paper proposes a new method called Direct Preference Optimization (DPO) for optimizing language models based on preference data.
  2. DPO simplifies the training of language models by using a straightforward loss function instead of a clunky process like RLHF.
  3. The use of preference data in training language models may impact value alignment and bias, especially in large models like GPT-4.